Ensemble rainfall forecasting with numerical weather prediction and radar‐based nowcasting models

The overall objective of this study is to improve the forecasting accuracy of the precipitation in the Singapore region by means of both rainfall forecasting and nowcasting. Numerical Weather Predication (NWP) and radar‐based rainfall nowcasting are two important sources for quantitative precipitation forecast. In this paper, an attempt to combine rainfall prediction from a high‐resolution mesoscale weather model and a radar‐based rainfall model was performed. Two rainfall forecasting methods were selected and examined: (i) the weather research and forecasting model (WRF); and (ii) a translation model (TM). The WRF model, at a high spatial resolution, was run over the domain of interest using the Global Forecast System data as initializing fields. Some heavy rainfall events were selected from data record and used to test the forecast capability of WRF and TM. Results obtained from TM and WRF were then combined together to form an ensemble rainfall forecasting model, by assigning weights of 0.7 and 0.3 weights to TM and WRF, respectively. This paper presented results from WRF and TM, and the resulting ensemble rainfall forecasting; comparisons with station data were conducted as well. It was shown that results from WRF are very useful as advisory of anticipated heavy rainfall events, whereas those from TM, which used information of rain cells already appearing on the radar screen, were more accurate for rainfall nowcasting as expected. The ensemble rainfall forecasting compares reasonably well with the station observation data. Copyright © 2012 John Wiley & Sons, Ltd.     

Seasonal ensemble generator for radar rainfall using copula and autoregressive model

Uncertainty analysis of radar rainfall enables stakeholders and users have a clear knowledge of the possible uncertainty associated with the rainfall products. Long-term empirical modeling of the relationship between radar and gauge measurements is an efficient and practical method to describe the radar rainfall uncertainty. However, complicated variation of synoptic conditions makes the radar-rainfall uncertainty model based on historical data hard to extend in the future state. A promising solution is to integrate synoptic regimes with the empirical model and explore the impact of individual synoptic regimes on radar rainfall uncertainty. This study is an attempt to introduce season, one of the most important synoptic factor, into the radar rainfall uncertainty model and proposes a seasonal ensemble generator for radar rainfall using copula and autoregressive model. We firstly analyze the histograms of rainfall-weighted temperature, the radar-gauge relationships, and Box and Whisker plots in different seasons and conclude that the radar rainfall uncertainty has strong seasonal dependence. Then a seasonal ensemble generator is designed and implemented in a UK catchment under a temperate maritime climate, which can fully model marginal distribution, spatial dependence, temporal dependence and seasonal dependence of radar rainfall uncertainty. To test its performance, 12 typical rainfall events (4 for each season) are chosen to generate ensemble rainfall values. In each time step, 500 ensemble members are produced and the values of 5th to 95th percentiles are used to derive the uncertainty bands. Except several outliers, the uncertainty bands encompass the observed gauge rainfall quite well. The parameters of the ensemble generator vary considerably for each season, indicating the seasonal ensemble generator reflects the impact of seasons on radar rainfall uncertainty. This study is an attempt to simultaneously consider four key features of radar rainfall uncertainty and future study will investigate their impacts on the outputs of hydrological models with radar rainfall as input or initial conditions.     

Vegetation response to rainfall seasonality and interannual variability in tropical dry forests

Projected changes in rainfall seasonality and interannual variability are expected to have severe impacts on arid and semi‐arid tropical vegetation, which is characterized by a fine‐tuned adaptation to extreme rainfall seasonality. To study the response of these ecosystems and the related changes in hydrological processes to changes in the amount and seasonality of rainfall, we focused on the caatinga biome, the typical seasonally dry forest in semi‐arid Northeast Brazil. We selected four sites across a gradient of rainfall amount and seasonality and analysed daily rainfall and biweekly Normalized Difference Vegetation Index (NDVI) data for hydrological years 2000 to 2014. Rainfall seasonal and interannual statistics were characterized by recently proposed metrics describing duration, timing and intensity of the wet season and compared to similar metrics of NDVI time series. The results show that the caatinga tends to have a more stable response with longer and less variable growing seasons (3.1 ± 0.1 months) compared to the duration wet seasons (2.0 ± 0.5 months). The ecosystem ability to buffer the interannual variability of rainfall is also evidenced by the stability in the timing of the growing season compared to the wet season, which results in variable delays (ranging from 0 to 2 months) between the peak of the rainfall season and the production of leaves by the ecosystem. The analyses show that the shape and size of the related hysteresis loops in the rainfall–NDVI relations are linked to the buffering effects of soil moisture and plant growth dynamics. Finally, model projections of vegetation response to different rainfall scenarios reveal the existence of a maximum in ecosystem productivity at intermediate levels of rainfall seasonality, suggesting a possible trade‐off in the effects of intensity (i.e. amount) and duration of the wet season on vegetation growth and related soil moisture dynamics and transpiration rates. Copyright © 2016 John Wiley & Sons, Ltd.     

Experimental Seasonal Forecast of Monsoon 2005 Using T170L42 AGCM on PARAM Padma

As a part of the Experimental Extended Range Monsoon Prediction Experiment, ensemble mode seasonal runs for the monsoon season of 2005 were made using the National Centre for Environmental Prediction (NCEP), T170L42 AGCM. The seasonal runs were made using six initial atmospheric conditions based on the NCEP operational analysis and with forecast monthly sea-surface temperature (SST) of the NCEP Coupled forecast system (CFS). These simulations were carried out on the PARAM Padma supercomputer of Centre for Development of Advanced Computing (C-DAC), India. The model climatology was prepared by integrating the model for ten years using climatological SST as the lower boundary. The climatology of the model compares well with the observed, in terms of the spatial distribution of rainfall over the Indian land mass. The model-simulated rainfall compares well with the Tropical Rainfall Measuring Mission (TRMM) estimates for the 2005 monsoon season. Compared to the model climatology (7.81 mm/day), the model had simulated a normal rainfall (7.75 mm/day) for the year 2005 which is in agreement with the observations (99% of long-term mean). However, the model could not capture the observed increase in September rainfall from that of a low value in August 2005. The circulation patterns simulated by the model are also comparable to the observed patterns. The ensemble mean onset is found to be nearer to the observed onset date within one pentad.     

Addressing uncertainty in reflectivity‐rainfall relations in mountain watersheds during summer convection

The use of precipitation estimates from weather radar reflectivity has become widespread in hydrologic predictions. However, uncertainty remains in the use of the nonlinear reflectivity–rainfall (Z‐R) relation, in particular for mountainous regions where ground validation stations are often lacking, land surface data sets are inaccurate and the spatial variability in many features is high. In this study, we assess the propagation of rainfall errors introduced by different Z‐R relations on distributed hydrologic model performance for four mountain basins in the Colorado Front Range. To do so, we compare spatially integrated and distributed rainfall and runoff metrics at seasonal and event time scales during the warm season when convective storms dominate. Results reveal that the basin simulations are quite sensitive to the uncertainties introduced by the Z‐R relation in terms of streamflow, runoff mechanisms and the water balance components. The propagation of rainfall errors into basin responses follows power law relationships that link streamflow uncertainty to the precipitation errors and streamflow magnitude. Overall, different Z‐R relations preserve the spatial distribution of rainfall relative to a reference case, but not the precipitation magnitude, thus leading to large changes in streamflow amounts and runoff spatial patterns at seasonal and event scales. Furthermore, streamflow errors from the Z‐R relation follow a typical pattern that varies with catchment scale where higher uncertainties exist for intermediate‐sized basins. The relatively high error values introduced by two operational Z‐R relations (WSR‐57 and NEXRAD) in terms of the streamflow response indicate that site‐specific Z‐R relations are desirable in the complex terrain region, particularly in light of other uncertainties in the modelling process, such as model parameter values and initial conditions. Copyright © 2012 John Wiley & Sons, Ltd.     

Long-term rainfall trends and change points in hot and cold arid regions of India

This study examined trends and change points in 100-year annual and seasonal rainfall over hot and cold arid regions of India. Using k-means clustering, 32 stations were classified into two clusters: the coefficient of variation for annual and seasonal rainfall was relatively high for Cluster-II compared to Cluster-I. Short-term and long-term persistence was more dominant in Cluster-II (entirely arid) and Cluster-I (partly arid), respectively. Trend tests revealed prominent increasing trends in annual and wet season rainfall of Cluster-II. Dry season rainfall increased by 1.09 mm year^?1 in the cold arid region. The significant change points in annual and wet season rainfall mostly occurred in the period 1941–1955 (hot and cold), and in the dry season in the period 1973–1975 (hot arid) and in 1949 (cold arid). The findings are useful for managing a surplus or deficiency of rainwater in the Indian arid region.

EDITOR A. Castellarin; ASSOCIATE EDITOR S. Kanae     

Simulation of daily rainfall scenarios with interannual and multidecadal climate cycles for South Florida

Concerns about the potential effects of anthropogenic climate change have led to a closer examination of how climate varies in the long run, and how such variations may impact rainfall variations at daily to seasonal time scales. For South Florida in particular, the influences of the low-frequency climate phenomena, such as the El Nino Southern Oscillation (ENSO) and the Atlantic Multi-decadal Oscillation (AMO), have been identified with aggregate annual or seasonal rainfall variations. Since the combined effect of these variations is manifest as persistent multi-year variations in rainfall, the question of modeling these variations at the time and space scales relevant for use with the daily time step-driven hydrologic models in use by the South Florida Water Management District (SFWMD) has arisen. To address this problem, a general methodology for the hierarchical modeling of low- and high-frequency phenomenon at multiple rain gauge locations is developed and illustrated. The essential strategy is to use long-term proxies for regional climate to first develop stochastic scenarios for regional climate that include the low-frequency variations driving the regional rainfall process, and then to use these indicators to condition the concurrent simulation of daily rainfall at all rain gauges under consideration. A newly developed methodology, called Wavelet Autoregressive Modeling (WARM), is used in the first step after suitable climate proxies for regional rainfall are identified. These proxies typically have data available for a century to four centuries so that long-term quasi-periodic climate modes of interest can be identified more reliably. Correlation analyses with seasonal rainfall in the region are used to identify the specific proxies considered as candidates for subsequent conditioning of daily rainfall attributes using a Non-homogeneous hidden Markov model (NHMM). The combined strategy is illustrated for the May–June–July (MJJ) season. The details of the modeling methods and results for the MJJ season are presented in this study.     

Towards flash-flood prediction in the dry Dead Sea region utilizing radar rainfall information

Flash-flood warning models can save lives and protect various kinds of infrastructure. In dry climate regions, rainfall is highly variable and can be of high-intensity. Since rain gauge networks in such areas are sparse, rainfall information derived from weather radar systems can provide useful input for flash-flood models. This paper presents a flash-flood warning model which utilizes radar rainfall data and applies it to two catchments that drain into the dry Dead Sea region. Radar-based quantitative precipitation estimates (QPEs) were derived using a rain gauge adjustment approach, either on a daily basis (allowing the adjustment factor to change over time, assuming available real-time gauge data) or using a constant factor value (derived from rain gauge data) over the entire period of the analysis. The QPEs served as input for a continuous hydrological model that represents the main hydrological processes in the region, namely infiltration, flow routing and transmission losses. The infiltration function is applied in a distributed mode while the routing and transmission loss functions are applied in a lumped mode. Model parameters were found by calibration based on the 5 years of data for one of the catchments. Validation was performed for a subsequent 5-year period for the same catchment and then for an entire 10-year record for the second catchment. The probability of detection and false alarm rates for the validation cases were reasonable. Probabilistic flash-flood prediction is presented applying Monte Carlo simulations with an uncertainty range for the QPEs and model parameters. With low probability thresholds, one can maintain more than 70% detection with no more than 30% false alarms. The study demonstrates that a flash-flood warning model is feasible for catchments in the area studied.     

Numerical simulation and data assimilation of the water-energy cycle over semiarid northeastern China

The default fractional vegetation cover and terrain height were replaced by the estimated fractional vegetation cover, which was calculated by the Normalized Difference Vegetation Index(NDVI) of Earth Observing System Moderate-Resolution Imaging Spectroradiometer(EOS-MODIS) and the Digital Elevation Model of the Shuttle Radar Topography Mission(SRTM) system. The near-surface meteorological elements over northeastern China were assimilated into the three-dimensional variational data assimilation system(3DVar) module in the Weather Research and Forecasting(WRF) model. The structure and daily variations of air temperature, humidity, wind and energy fields over northeastern China were simulated using the WRF model. Four groups of numerical experiments were performed, and the simulation results were analyzed of latent heat flux, sensible heat flux, and their relationships with changes in the surface energy flux due to soil moisture and precipitation over different surfaces. The simulations were compared with observations of the stations Tongyu, Naiman, Jinzhou, and Miyun from June to August, 2009. The results showed that the WRF model achieves high-quality simulations of the diurnal characteristics of the surface layer temperature, wind direction, net radiation, sensible heat flux, and latent heat flux over semiarid northeastern China in the summer. The simulated near-surface temperature, relative humidity, and wind speed were improved in the data assimilation case(Case 2) compared with control case(Case 1). The simulated sensible heat fluxes and surface heat fluxes were improved by the land surface parameterization case(Case 3) and the combined case(Case 4). The simulated temporal variations in soil moisture over the northeastern arid areas agree well with observations in Case 4, but the simulated precipitation should be improved in the WRF model. This study could improve the land surface parameters by utilizing remote sensing data and could further improve atmospheric elements with a data assimilation system. This work provides an effective attempt at combining multi-source data with different spatial and temporal scales into numerical simulations. The assimilation datasets generated by this work can be applied to research on climate change and environmental monitoring of arid lands, as well as research on the formation and stability of climate over semiarid areas.     

Entropy theory for analysing water resources in northeastern region of Brazil

Using the Shannon entropy, the space–time variability of rainfall and streamflow was assessed for daily rainfall and streamflow data for a 10-year period from 189 stations in the northeastern region of Brazil. Mean values of marginal entropy were computed for all observation stations and entropy maps were then constructed for delineating annual and seasonal characteristics of rainfall and streamflow. The Mann-Kendall test was used to evaluate the long-term trend in marginal entropy as well as relative entropy for two sample stations. The marginal entropy values of rainfall and streamflow were higher for locations and periods with the highest amounts of rainfall. The entropy values were higher where rainfall was higher. This was because the probability distributions of rainfall and the resulting streamflow were more uniform and less skewed. The Shannon entropy produced spatial patterns which led to a better understanding of rainfall and streamflow characteristics throughout the northeastern region of Brazil. The total relative entropy indicated that rainfall and streamflow carried the same information content at annual and rainy season time scales.     

Variabilités interannuelle et intra-saisonnière des pluies aux échelles hydrologiques. La mousson ouest-africaine en climat soudanien / Seasonal cycle and interannual variability of rainfall at hydrological scales. The West African monsoon in a Sudanese climate

Abstract

West African rainfall is characterized by a strong variability, both at decadal and interannual scales. In order to quantify the hydrological impacts of such a variability, analysis of rainfall patterns at fine scales is highly essential. This diagnostic study aims to characterize the Sudanese rainfall regime at hydrological scales, using a raingauge data set collected on the upper Oueme River catchment (Benin) between 1950 and 2002. A long-term drought is observed during the 1970s and 1980s, as in the Sahel. However, the interannual variability remains significant in the Sudanese region. The study of the seasonal cycle, based on the distinction between the oceanic and continental monsoon regimes, shows that the majority of rainfall changes occur in the continental regime. On the one hand, the rainfall peak associated with this regime that has been observed for the last 50 years has occurred increasingly earlier in the season. On the other hand, the annual rainfall deficit is mainly linked to the decrease in the number of large events during the continental part of the season.     

Evolution of Vertical Moist Thermodynamic Structure Associated with the Indian Summer Monsoon 2010 in a Regional Climate Model

The 2010 boreal summer marked a worldwide abnormal climate. An unprecedented heat wave struck East Asia in July and August 2010. In addition to this, the tropical Indian Ocean was abnormally warm during the summer of 2010. Several heavy rainfall events and associated floods were also reported in the Indian monsoon region. During the season, the monsoon trough (an east–west elongated area of low pressure) was mostly located south of its normal position and monsoon low pressure systems moved south of their normal tracks. This resulted in an uneven spatial distribution with above-normal rainfall over peninsular and Northwest India, and deficient rainfall over central and northeastern parts of India, thus prediction (and simulation) of such anomalous climatic summer season is important. In this context, evolution of vertical moist thermodynamic structure associated with Indian summer monsoon 2010 is studied using regional climate model, reanalysis and satellite observations. This synergised approach is the first of its kind to the best of our knowledge. The model-simulated fields (pressure, temperature, winds and precipitation) are comparable with the respective in situ and reanalysis fields, both in intensity and geographical distribution. The correlation coefficient between model and observed precipitation is 0.5 and the root-mean-square error (RMSE) is 4.8 mm day^?1. Inter-comparison of model-simulated fields with satellite observations reveals that the midtropospheric temperature [Water vapour mixing ratio (WVMR)] has RMSE of 0.5 K (1.6 g kg^?1), whereas the surface temperature (WVMR) has RMSE of 3.4 K (2.2 g kg^?1). Similarly, temporal evolution of vertical structure of temperature with rainfall over central Indian region reveals that the baroclinic nature of monsoon is simulated by the model. The midtropospheric warming associated with rainfall is captured by the model, whereas the model failed to capture the surface response to high and low rainfall events. The model has strong water vapour loading in the whole troposphere, but weaker coherent response with rainfall compared to observations. Thus, strong water vapour loading and overestimation of rainfall are reported in the model. This study put forward that the discrepancy in the model-simulated structure may be reduced by assimilation of satellite observations.     

Evaluating the performance of ground-based and remotely sensed near real-time rainfall fields from a hydrological perspective

Abstract

The South African Weather Service (SAWS) issues routine experimental, near real-time rainfall maps from daily raingauge networks, radar networks and satellite images, as well as merged rainfall fields. These products are potentially useful for near real-time forecasting, especially in areas of fast hydrological response, and also to simulate the “now state” of various hydrological state variables such as soil moisture content, streamflow, and reservoir inflows. The purpose of this paper is to evaluate their skill as inputs to hydrological simulations and, in particular, the skill of the merged field in terms of better hydrological results relative to the individual products. Rainfall fields derived from raingauge, radar, satellite, conditioned satellite and the merged (gauge/radar/satellite) were evaluated for two selected days with relatively high amounts of rainfall, as well as for a continuous period of 90 days in the Mgeni catchment, South Africa. Streamflows simulated with the ACRU model indicate that the use of raingauge as well as merged fields of satellite/raingauge and satellite/radars/raingauge provides relatively realistic rainfall results, without much difference in their hydrological outputs, whereas the radar and raw satellite information by themselves cannot be used in operational hydrological application in their current status.

Citation Ghile, Y., Schulze, R. & Brown, C. (2010) Evaluating the performance of ground-based and remotely sensed near real-time rainfall fields from a hydrological perspective. Hydrol. Sci. J. 55(4), 497–511.     

Adjustment of wind-drift effect for real-time systematic error correction in radar rainfall data

An effective bias correction procedure using gauge measurement is a significant step for radar data processing to reduce the systematic error in hydrological applications. In these bias correction methods, the spatial matching of precipitation patterns between radar and gauge networks is an important premise. However, the wind-drift effect on radar measurement induces an inconsistent spatial relationship between radar and gauge measurements as the raindrops observed by radar do not fall vertically to the ground. Consequently, a rain gauge does not correspond to the radar pixel based on the projected location of the radar beam. In this study, we introduce an adjustment method to incorporate the wind-drift effect into a bias correlation scheme. We first simulate the trajectory of raindrops in the air using downscaled three-dimensional wind data from the weather research and forecasting model (WRF) and calculate the final location of raindrops on the ground. The displacement of rainfall is then estimated and a radar–gauge spatial relationship is reconstructed. Based on this, the local real-time biases of the bin-average radar data were estimated for 12 selected events. Then, the reference mean local gauge rainfall, mean local bias, and adjusted radar rainfall calculated with and without consideration of the wind-drift effect are compared for different events and locations. There are considerable differences for three estimators, indicating that wind drift has a considerable impact on the real-time radar bias correction. Based on these facts, we suggest bias correction schemes based on the spatial correlation between radar and gauge measurements should consider the adjustment of the wind-drift effect and the proposed adjustment method is a promising solution to achieve this.     

Extreme value modelling of daily areal rainfall over Mediterranean catchments in a changing climate

Heavy rainfall events during the fall season are causing extended damages in Mediterranean catchments. A peaks‐over‐threshold model is developed for the extreme daily areal rainfall occurrence and magnitude in fall over six catchments in Southern France. The main driver of the heavy rainfall events observed in this region is the humidity flux (FHUM) from the Mediterranean Sea. Reanalysis data are used to compute the daily FHUM during the period 1958–2008, to be included as a covariate in the model parameters. Results indicate that the introduction of FHUM as a covariate can improve the modelling of extreme areal precipitation. The seasonal average of FHUM can improve the modelling of the seasonal occurrences of heavy rainfall events, whereas daily FHUM values can improve the modelling of the events magnitudes. In addition, an ensemble of simulations produced by five different general circulation models are considered to compute FHUM in future climate with the emission scenario A1B and hence to evaluate the effect of climate change on the heavy rainfall distribution in the selected catchments. This ensemble of climate models allows the evaluation of the uncertainties in climate projections. By comparison to the reference period 1960–1990, all models project an amplification of the mean seasonal FHUM from the Mediterranean Sea for the projection period 2070–2099, on average by +22%. This increase in FHUM leads to an increase in the number of heavy rainfall events, from an average of 2.55 events during the fall season in present climate to 3.57 events projected for the period 2070–2099. However, the projected changes have limited effects on the magnitude of extreme events, with only a 5% increase in the median of the 100‐year quantiles. Copyright © 2011 John Wiley & Sons, Ltd.     

Interannual climate variability in South America: impacts on seasonal precipitation, extreme events, and possible effects of climate change

Interannual variability is an important modulator of synoptic and intraseasonal variability in South America. This paper seeks to characterize the main modes of interannual variability of seasonal precipitation and some associated mechanisms. The impact of this variability on the frequency of extreme rainfall events and the possible effect of anthropogenic climate change on this variability are reviewed. The interannual oscillations of the annual total precipitation are mainly due to the variability in austral autumn and summer. While autumn is the dominant rainy season in the northern part of the continent, where the variability is highest (especially in the northeastern part), summer is the rainy season over most of the continent, thanks to a summer monsoon regime. In the monsoon season, the strongest variability occurs near the South Atlantic Convergence Zone (SACZ), which is one of the most important features of the South American monsoon system. In all seasons but summer, the most important source of variability is ENSO (El Ni?o Southern Oscillation), although ENSO shows a great contribution also in summer. The ENSO impact on the frequency of extreme precipitation events is also important in all seasons, being generally even more significant than the influence on seasonal rainfall totals. Climate change associated with increasing emission of greenhouse gases shows potential to impact seasonal amounts of precipitation in South America, but there is still great uncertainty associated with the projected changes, since there is not much agreement among the models’ outputs for most regions in the continent, with the exception of southeastern South America and southern Andes. Climate change can also impact the natural variability modes of seasonal precipitation associated with ENSO.     

WRF模式不同陆面方案对一次暴雨事件模拟的影响

本文利用中尺度模式Weather Research and Forecasting Model (WRF) 3.1版本及National Centers for Environmental Prediction (NCEP)分析资料,就2003年6月下旬我国江淮及南方地区的强降水事件, 以24 h短期天气模拟的方式,研究了模式中四个不同陆面方案对降水模拟的影响.结果表明,此次暴雨事件模拟对不同陆面方案是比较敏感的,模拟区域内雨量级别越高,不同方案的TS评分差异就越大,较大范围雨量可存在30%的差异,四种方案的暴雨中心值可存在100%～150%的较大差别;不同陆面方案还导致了模拟平均感热通量及潜热通量的系统性差异,这些差异的分布具有地域特点;陆面方案通过两种机理对模拟降水产生重要影响,即主要影响地表蒸发量,以及主要影响低层环流及水汽辐合,从而分别影响模拟的较大范围降水(如,平均约7%、最大约30%的较大范围雨量差异)及包含模拟降水中心的较小范围暴雨(如,方案间暴雨中心雨量可存在100%～150%的较大差别).可见,不同陆面过程可从不同空间尺度、不同程度上影响暴雨天气,改进陆面方案可以提高WRF模式对暴雨的模拟能力.     

The impact of different rainfall products on landscape modelling simulations

Rainfall products can contain significantly different spatiotemporal estimates, depending on their underlying data and final constructed resolution. Commonly used products, such as rain gauges, rain gauge networks, and weather radar, differ in their information content regarding intensities, spatial variability, and natural climatic variability, therefore producing different estimates. Landscape evolution models (LEMs) simulate the geomorphic changes in landscapes, and current models can simulate timeframes from event level to millions of years and some use rainfall inputs to drive them. However, the impact of different rainfall products on LEM outputs has never been considered. This study uses the STREAP rainfall generator, calibrated using commonly used rainfall observation products, to produce longer rainfall records than the observations to drive the CAESAR-Lisflood LEM to examine how differences in rainfall products affect simulated landscapes. The results show that the simulation of changes to basin geomorphology is sensitive to the differences between rainfall products, with these differences expressed linearly in discharges but non-linearly in sediment yields. Furthermore, when applied over a 1500-year period, large differences in the simulated long profiles were observed, with the simulations producing greater sediment yields showing erosion extending further downstream. This suggests that the choice of rainfall product to drive LEMs has a large impact on the final simulated landscapes. The combination of rainfall generator model and LEMs represents a potentially powerful method for assessing the impacts of rainfall product differences on landscapes and their short- and long-term evolution. © 2020 The Authors. Earth Surface Processes and Landforms published by John Wiley & Sons Ltd     

Occurrence,shape and movement of daily rainfall in the Huai Basin,China

The upper reaches of the Huai River in Central China are located in the East Asian monsoon region. Strong seasonality, as well as large interannual variability of rainfall, causes floods and an uneven supply of water. In order to conserve the water and mitigate the floods, dams and flood protection structures are constructed. Their design requires information about the rainfall. Daily observations from 1957 to 1986 from 78 rain gauges were used to study shape, orientation, movement and geographical and seasonal occurrence of storms in the 79 000 km² study area. The rainfall characteristics were described using graphical plots, cross‐ and autocorrelation. Storms larger than 50 mm/day were found to occur from February to November, whereas storms exceeding 350 mm/day were confined to the main rainfall season from late June to mid‐August. The southern part of the study area experienced a break in the rainfall season in late July, corresponding to the seasonal northward shift of the rain belt. A weekly periodicity of 7–8 days for rainfall was found during June–July, but not during August–September. During the whole period June–September, the spatial pattern of daily rainfall revealed an elongated shape, more pronounced during June–July than August–September. The rainfall area was orientated approximately from WSW to ENE during the whole period, and showed an anticlockwise rotation of about 16° per day during June–July. The cross‐correlation analysis revealed that the rainfall area moved about 100 km/day eastward. These results and an investigation of meteorological maps indicate that the spatial correlation pattern of daily rainfall is produced by cold fronts on the Mei‐Yu front. Suggestions are made as to how to use the results for the construction of design rainfalls in the study area. Copyright © 1999 John Wiley & Sons, Ltd.     

Exploring the effect of data assimilation by WRF‐3DVar for numerical rainfall prediction with different types of storm events

The mesoscale Numerical Weather Prediction (NWP) model is gaining popularity among the hydrometeorological community in providing high‐resolution rainfall forecasts at the catchment scale. Although the performance of the model has been verified in capturing the physical processes of severe storm events, the modelling accuracy is negatively affected by significant errors in the initial conditions used to drive the model. Several meteorological investigations have shown that the assimilation of real‐time observations, especially the radar data can help improve the accuracy of the rainfall predictions given by mesoscale NWP models. The aim of this study is to investigate the effect of data assimilation for hydrological applications at the catchment scale. Radar reflectivity together with surface and upper‐air meteorological observations is assimilated into the Weather Research and Forecasting (WRF) model using the three‐dimensional variational data‐assimilation technique. Improvement of the rainfall accumulation and its temporal variation after data assimilation is examined for four storm events in the Brue catchment (135.2 km²) located in southwest England. The storm events are selected with different rainfall distributions in space and time. It is found that the rainfall improvement is most obvious for the events with one‐dimensional evenness in either space or time. The effect of data assimilation is even more significant in the innermost domain which has the finest spatial resolution. However, for the events with two‐dimensional unevenness of rainfall, i.e. the rainfall is concentrated in a small area and in a short time period, the effect of data assimilation is not ideal. WRF fails in capturing the whole process of the highly convective storm with densely concentrated rainfall in a small area and a short time period. A shortened assimilation time interval together with more efficient utilisation of the weather radar data might help improve the effectiveness of data assimilation in such cases. Copyright © 2012 John Wiley & Sons, Ltd.